Frost resistance prediction for rubberized concrete based on artificial neural network - Chun Fu; Ming Li (Discover Applied Sciences, 2024)
Abstract Using waste rubber to partially replace fine aggregate to make rubber concrete can not only reduce black pollution to alleviate the dilemma of natural sand resource depletion, but also improve the frost resistance of concrete, which is undoubtedly a win–win solution. Aim to promote the application of rubber concrete seasonal cold regions, it is of great significance to evaluate and predict its frost-resistance. Different from ordinary concrete, the existence of rubber changes the inherent characteristics of concrete to varying degrees, which makes the durability of rubber concrete more complicated and the establishment of prediction models more challenging. In this paper, an artificial neural network (ANN) model was proposed to predict the frost-resistance of rubberized concrete. Using water-cement ratio, cement, sand, sand rate, rubber content and the number of freeze–thaw cycles as input variables and relative dynamic elastic modulus as output variables, a three-layer BP neural network (BPNN) prediction model with a hidden layer was established on the basis of a large number of experimental data of another author. The prediction results show that the proposed BPNN model has a strong ability to predict the frost resistance of rubberized concrete with satisfactory accuracy (R2 = 0.9825, MAPE = 1.5609%), which opens up a new way to improve the prediction accuracy of frost-resistance for rubberized concrete.